RAG vs Agent Memory: What Should Your AI Product Use?
A technical comparison of retrieval-augmented generation and agent memory for AI products, workflows, and long-running context.
RAG vs Agent Memory: What Should Your AI Product Use?
RAG and agent memory both help AI systems use context beyond the immediate prompt, but they solve different problems.
RAG, or retrieval-augmented generation, usually pulls relevant information from a document or data store at query time. Agent memory stores useful context from prior interactions, workflows, or user preferences.
Use RAG for
- Knowledge bases and documentation.
- Search across large corpora.
- Source-grounded answers.
- Frequently updated reference material.
Use agent memory for
- User preferences.
- Ongoing projects.
- Workflow state.
- Repeated decisions and personal context.
Many AI products need both. RAG brings in external knowledge. Memory preserves continuity.
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